# Bridging Generative Networks with the Common Model of Cognition

arXiv: https://arxiv.org/abs/2403.18827 · HTML: https://mlsanity.com/paper/2403.18827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019)

## Built on by (most cited first, 1 of 1)

- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)

## Abstract (arXiv)

This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production system, which handles higher-level reasoning based on the shadow productions' output. Implementing this novel structure within the Common Model allows for a seamless connection between cognitive architectures and generative neural networks.